Overcoming the source identification barrier: A critical tool for realistic risk assessment of CeO₂ nanoparticles in...

The environmental risk assessment of CeO₂ engineered nanoparticles (ENPs) is limited by the inability to distinguish their specific exposure from ubiquitous natural counterparts (NNPs) in agroecosystems. Here, we developed an analytical framework integrating single-particle inductively coupled plasma time-of-flight mass spectrometry (spICP-TOFMS) with machine learning (ML) to enable source discrimination of Ce-containing particles in a soil-plant system. The method successfully differentiated CeO₂ ENPs from NNPs in both soil and maize (Zea mays L.) tissues against a natural Ce background (67.4 mg/kg). Source-resolved revealed that maize roots accumulate CeO₂ ENPs in a dose-dependent manner, with particle numbers increasing by one to two orders of magnitude relative to the control, whereas NNP uptake remained stable. In contrast, ENP levels in shoots were significantly lower, indicating limited upward translocation. These results demonstrate that separating engineered from natural nanoparticle fractions can improve exposure assessment beyond total element measurements. The proposed framework provides a practical approach for generating source-resolved exposure data and supports improved environmental risk assessment of engineered nanomaterials, although further standardization, validation, and sensitivity improvements are required for broader application.